Classification of ECG arrhythmias based on statistical and time-frequency features

Conference item

In this paper a new approach to accurately classify ECG arrhythmias through a combination of the wavelet transform and artificial neural network is presented. Three kinds of features in a very computationally efficient manner are computed as follows: 1-Joint time-frequency features (discrete wavelet transform coefficients). 2-Time domain features (R-R intervals). 3-Statistical feature (form factor). Using these features, the limitations of other methods in classifying multiple kinds of arrhythmia with high accuracy for all of them at once are overcome. Finally, a cascade classifier including two ANNs has been designed. Considering the whole MIT-BIH arrhythmia database, 10 kinds of arrhythmia were classified. The overall accuracy of classification of the proposed approach is above 90%.

Authors: Kadbi, M; Hashemi, J; Mohseni, H; Maghsoudi, A

• Host title: IET Conference Publications
• Issue: 520
• Pages: 24-24
• Publication date: 2006-01-01
• DOI: 10.1049/cp:20060376
• ISBN-10: 0863416586
• ISBN-13: 9780863416583
• Keywords: Form factor; Wavelet coeffi-cients; Arrhythmia; Principle component analysis; Classification